1. Field of the Invention
The present invention relates to image registration, and more particularly to methods and systems for aligning images using minimum entropic graphs.
2. Description of the Related Art
Registration or geometric alignment of images is a fundamental problem in image processing. Registration can be defined as the process of determining the point-to-point correspondence between two images. In essence, image registration is the process of aligning images so that corresponding features can be easily related. Registration algorithms find widespread use in image-processing applications. Typical applications include medical/biomedical image analysis, computer vision and military applications. In many such applications it is necessary to register multiple images of the same scene acquired by different sensors, or images taken by the same sensor but at different times or from different viewpoints. For example, it is common for medical images to be captured by imaging the same patient with different modalities or a single modality at different times. Goshtasby provides image registration techniques for remote sensing, industrial and medical applications and presents their underlying algorithms. Goshtasby, A. Ardeshir, 2-D and 3-D Image Registration: for Medical, Remote Sensing, and Industrial Applications, John Wiley & Sons, Inc., February 2005.
In recent years, medical imaging has experienced an explosive growth due to advances in imaging modalities such as X-rays, computed tomography (CT), Doppler ultrasound, magnetic resonance (MR), positron emission tomography (PET) and single photon emission tomography (SPET). Typically, medical images are made up of a rectangular array of small square or rectangular elements or pixels, each of which has an associated image intensity value. Registration makes it possible to compare information in a reference image and a sensed image (also known as a target image) pixel by pixel. Two-dimensional (2-D) slices can be combined to generate a three-dimensional (3-D) volumetric model, and many images are now acquired directly as 3-D volumes. Each pixel in the 2-D slices corresponds to a small volume element or voxel in the 3-D volume. Voxel-based registration algorithms have been successfully applied to a range of image types. Hajnal edits a collection of papers reviewing methods particularly suitable for registration of medical images. Hajnal, J. V., Hill, D. L. G., Hawkes, D. J., Eds., Medical Image Registration, CRC Press LLC, 2001.
The registration of multimodality images is fundamental to medical image interpretation and analysis. By registering multimodality images, the fusion of multimodality information becomes possible. For example, it is beneficial to visualize functional data overlaid on anatomical data in the study of brain function, in which MR images provide anatomical information and functional information can be obtained from PET images. By registering the MR and PET brain images, functional and anatomical information can be combined so that brain regions of abnormal function can be anatomically located. Similarly, a range of application areas exist for monomodality registration, such as treatment verification by comparison of pre- and post-intervention images.
Registration establishes correspondence between medical images and physical space in image-guided interventions. In recent years, image registration techniques have entered routine clinical use in image-guided neurosurgery systems and computer-assisted orthopedic surgery. For example, recent research shows that computer-aided systems with interactive display of 3D bone models created from preoperative CT studies and tracked in real time can improve the accuracy of orthopedic procedures.
FIG. 1 is a flowchart illustrating a conventional registration algorithm. The task of image registration has three key components: the registration metric that quantifies the similarity between two images; the transformation space that determines the allowed spatial transformations; and the optimization scheme for searching for the transformation that maximizes (or minimizes) the registration metric. Referring to FIG. 1, the registration algorithm tries to find a spatial transformation that “warps” the floating image to the geometry of the reference image. Digital image warping uses the geometric transformation techniques from such fields as medical imaging, computer vision, and computer graphics. The optimization scheme searches to find the best transformation that will maximize the similarity measure (also known in the art as an alignment measure) or minimize the dissimilarity measure. Nocedal, J. and Wright, S. J., Numerical Optimization (Springer Series in Operations Research), Springer-Verlag, 1999.